We summarize in this paper works about the management of data in a distributed manner based on Webdamlog, a datalog-extension. We point to relevant articles on these works. More references may be found there. 1 The Webdamlog approach Information of interest may be found on the Web in a variety of forms, in many systems, and with different access protocols. Today, the control and management of the diversity of data and tasks in this setting are beyond the skills of casual users [1]. Facing similar issues, companies see the cost of managing and integrating information skyrocketing. We are concerned with the management of Web data in place in a distributed manner, with a possibly large number of autonomous, heterogeneous systems collaborating to support certain tasks. We summarize in this paper works in this setting around Webdamlog and point to the relevant articles on it. The thesis is that managing the richness and diversity of data residing on the Web can be tamed using a holistic approach based on a distributed knowledge base. Our approach is to represent all Web information as logical facts, and Web data management tasks as logical rules. A variety of complex data management tasks that currently require intense work and deep expertise may then greatly benefit from the automatic reasoning provided by inference engines, operating over the distributed Web knowledge base: for instance, information access, access control, knowledge acquisition and dissemination. We propose to express the peers logic in Webdamlog, a datalog-style rule-based language. In Webdamlog, peers exchange facts (for information) and rules (in place of code). The use of declarative rules provides the following advantages. Peers may perform automatic reasoning using the available knowledge. Because the model is formally defined, it becomes possible to prove (or disprove) desirable properties. Because the model is based on a datalog-style language, query processing can benefit from optimization techniques. Because the model represents provenance and time, the quality of data can be better controlled. Because We thank all the researchers who participated in the Webdamlog project and in particular ,